Software Effort Interval Prediction via Bayesian Inference and Synthetic Bootstrap Resampling
Software Effort Interval Prediction via Bayesian Inference and Synthetic Bootstrap Resampling
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DOI：
10.1145/3295700
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发表时间：
2019-01
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影响因子：
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通讯作者：
Liyan Song;Leandro L. Minku;X. Yao
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文献类型：
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作者：
Liyan Song;Leandro L. Minku;X. Yao
软件工作量估计（SEE）通常遭受固有的不确定性所产生的预测模型的限制和数据噪声。单纯依靠点估计可能会忽略不确定因素，导致项目经理（PM）做出错误的决策。具有置信水平（CL）的预测区间（PI）呈现了对现实的更合理的表示，可能有助于PM做出更明智的决策，并在这些决策中实现更大的灵活性。然而，现有的PI方法要么有很强的局限性，或无法提供信息PI。为了开发一个“更好”的努力预测，我们提出了一种新的PI估计称为合成Bootstrap集成的相关向量机（SynB-RVM），采用Bootstrap的响应产生多个RVM模型的基础上修改的训练袋，其复制的数据项目被替换为他们的合成同行。然后，我们提供了三种方法来组装这些RVM模型到一个最终的概率的努力预测，从中可以生成具有CL的PI。当用作点估计时，SynB-RVM可以显着优于或具有类似的性能与其他研究方法相比。当作为一个不确定的预测，SynB-RVM可以实现显着更窄的PI相比，它的基础学习RVM。它的命中率和相对宽度并不差于其他比较的方法，可以提供不确定的估计。
Software effort estimation (SEE) usually suffers from inherent uncertainty arising from predictive model limitations and data noise. Relying on point estimation only may ignore the uncertain factors and lead project managers (PMs) to wrong decision making. Prediction intervals (PIs) with confidence levels (CLs) present a more reasonable representation of reality, potentially helping PMs to make better-informed decisions and enable more flexibility in these decisions. However, existing methods for PIs either have strong limitations or are unable to provide informative PIs. To develop a “better” effort predictor, we propose a novel PI estimator called Synthetic Bootstrap ensemble of Relevance Vector Machines (SynB-RVM) that adopts Bootstrap resampling to produce multiple RVM models based on modified training bags whose replicated data projects are replaced by their synthetic counterparts. We then provide three ways to assemble those RVM models into a final probabilistic effort predictor, from which PIs with CLs can be generated. When used as a point estimator, SynB-RVM can either significantly outperform or have similar performance compared with other investigated methods. When used as an uncertain predictor, SynB-RVM can achieve significantly narrower PIs compared to its base learner RVM. Its hit rates and relative widths are no worse than the other compared methods that can provide uncertain estimation.